Machine Learning

Papers filed under cs.LG on arXiv, each one already summarized by Paperlayer. Open any of them to read the summary beside the original PDF, with every point linked to the line, figure, or table it came from.

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8,101 to 8,160 of 20,193

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar +3
cs.LGcs.AIcs.NEarXiv:1706.02275v42017
Learning Combinatorial Optimization Algorithms over Graphs
Hanjun Dai, Elias B. Khalil, Yuyu Zhang +2
cs.LGstat.MLarXiv:1704.01665v42017
Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network
ByeoungDo Kim, Chang Mook Kang, Seung Hi Lee +4
cs.LGarXiv:1704.07049v22017
Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh +3
cs.LGstat.MLarXiv:1703.06114v32017
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing
Jie Xu, Lixing Chen, Shaolei Ren
cs.LGcs.NIarXiv:1703.06060v12017
Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning
Stefan Elfwing, Eiji Uchibe, Kenji Doya
cs.LGarXiv:1702.03118v32017
An Introduction to Deep Learning for the Physical Layer
Timothy J. O'Shea, Jakob Hoydis
cs.ITcs.LGcs.NIarXiv:1702.00832v22017
Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer
Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz +4
cs.LGcs.CLcs.NEarXiv:1701.06538v12017
Learning to Invert: Signal Recovery via Deep Convolutional Networks
Ali Mousavi, Richard G. Baraniuk
stat.MLcs.AIcs.ITarXiv:1701.03891v12017
Machine Learning of Linear Differential Equations using Gaussian Processes
Maziar Raissi, George Em. Karniadakis
cs.LGmath.NAstat.MLarXiv:1701.02440v12017
Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data
Anuj Karpatne, Gowtham Atluri, James Faghmous +6
cs.LGcs.AIstat.MLarXiv:1612.08544v22016
Understanding Deep Neural Networks with Rectified Linear Units
Raman Arora, Amitabh Basu, Poorya Mianjy +1
cs.LGcond-mat.dis-nncs.AIarXiv:1611.01491v62016
Product-based Neural Networks for User Response Prediction
Yanru Qu, Han Cai, Kan Ren +4
cs.LGcs.IRarXiv:1611.00144v12016
A Survey of Multi-View Representation Learning
Yingming Li, Ming Yang, Zhongfei Zhang
cs.LGcs.CVcs.IRarXiv:1610.01206v52016
Deep Visual Foresight for Planning Robot Motion
Chelsea Finn, Sergey Levine
cs.LGcs.AIcs.CVarXiv:1610.00696v22016
Adversarial examples in the physical world
Alexey Kurakin, Ian Goodfellow, Samy Bengio
cs.CVcs.CRcs.LGarXiv:1607.02533v42016
Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks
Sabrina Müller, Onur Atan, Mihaela van der Schaar +1
cs.NIcs.LGarXiv:1606.04236v22016
Going Deeper with Contextual CNN for Hyperspectral Image Classification
Hyungtae Lee, Heesung Kwon
cs.CVcs.LGarXiv:1604.03519v32016
A survey of sparse representation: algorithms and applications
Zheng Zhang, Yong Xu, Jian Yang +2
cs.CVcs.LGarXiv:1602.07017v12016
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
cs.LGcs.AIstat.MLarXiv:1602.04938v32016
Benefits of depth in neural networks
Matus Telgarsky
cs.LGcs.NEstat.MLarXiv:1602.04485v22016
Variational Inference: A Review for Statisticians
David M. Blei, Alp Kucukelbir, Jon D. McAuliffe
stat.COcs.LGstat.MLarXiv:1601.00670v92016
The Power of Depth for Feedforward Neural Networks
Ronen Eldan, Ohad Shamir
cs.LGcs.NEstat.MLarXiv:1512.03965v42015
The Limitations of Deep Learning in Adversarial Settings
Nicolas Papernot, Patrick McDaniel, Somesh Jha +3
cs.CRcs.LGcs.NEarXiv:1511.07528v12015
The Extreme Value Machine
Ethan M. Rudd, Lalit P. Jain, Walter J. Scheirer +1
cs.LGarXiv:1506.06112v42015
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
James Martens, Roger Grosse
cs.LGcs.NEstat.MLarXiv:1503.05671v72015
LSTM: A Search Space Odyssey
Klaus Greff, Rupesh Kumar Srivastava, Jan Koutník +2
cs.NEcs.LGarXiv:1503.04069v22015
Deep Learning and the Information Bottleneck Principle
Naftali Tishby, Noga Zaslavsky
cs.LGarXiv:1503.02406v12015
Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval
Hamid Palangi, Li Deng, Yelong Shen +5
cs.CLcs.IRcs.LGarXiv:1502.06922v32015
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Sergey Ioffe, Christian Szegedy
cs.LGarXiv:1502.03167v32015
Learning to Generate Chairs, Tables and Cars with Convolutional Networks
Alexey Dosovitskiy, Jost Tobias Springenberg, Maxim Tatarchenko +1
cs.CVcs.LGcs.NEarXiv:1411.5928v42014
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio
cs.CLcs.LGcs.NEarXiv:1409.0473v72014
Learning Deep Representation for Face Alignment with Auxiliary Attributes
Zhanpeng Zhang, Ping Luo, Chen Change Loy +1
cs.CVcs.LGarXiv:1408.3967v42014
Learning to Deblur
Christian J. Schuler, Michael Hirsch, Stefan Harmeling +1
cs.CVcs.LGarXiv:1406.7444v12014
Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms
Wei Chen, Yajun Wang, Yang Yuan +1
cs.LGarXiv:1407.8339v62014
Auto-Encoding Variational Bayes
Diederik P Kingma, Max Welling
stat.MLcs.LGarXiv:1312.6114v112013
Dropout improves Recurrent Neural Networks for Handwriting Recognition
Vu Pham, Théodore Bluche, Christopher Kermorvant +1
cs.CVcs.LGcs.NEarXiv:1312.4569v22013
Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals
Jun Fang, Yanning Shen, Hongbin Li +1
cs.ITcs.LGstat.MLarXiv:1311.2150v12013
Stochastic blockmodel approximation of a graphon: Theory and consistent estimation
Edoardo M Airoldi, Thiago B Costa, Stanley H Chan
stat.MEcs.LGcs.SIarXiv:1311.1731v22013
Deep Learning Through the Lens of Example Difficulty
Robert J. N. Baldock, Hartmut Maennel, Behnam Neyshabur
cs.LGstat.MLarXiv:2106.09647v22021
Domain Generalization via Invariant Feature Representation
Krikamol Muandet, David Balduzzi, Bernhard Schölkopf
stat.MLcs.LGarXiv:1301.2115v12013
The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains
David I Shuman, Sunil K. Narang, Pascal Frossard +2
cs.DMcs.LGcs.SIarXiv:1211.0053v22012
Deep Learning for Detecting Robotic Grasps
Ian Lenz, Honglak Lee, Ashutosh Saxena
cs.LGcs.CVcs.ROarXiv:1301.3592v62013
Equivalence of distance-based and RKHS-based statistics in hypothesis testing
Dino Sejdinovic, Bharath Sriperumbudur, Arthur Gretton +1
stat.MEcs.LGmath.STarXiv:1207.6076v32012
Sparse Distributed Learning Based on Diffusion Adaptation
Paolo Di Lorenzo, Ali H. Sayed
cs.LGcs.DCarXiv:1206.3099v22012
Summaries:한국어
Practical Bayesian Optimization of Machine Learning Algorithms
Jasper Snoek, Hugo Larochelle, Ryan P. Adams
stat.MLcs.LGarXiv:1206.2944v22012
Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks
Jianshu Chen, Ali H. Sayed
math.OCcs.ITcs.LGarXiv:1111.0034v32011
Spectral Methods for Learning Multivariate Latent Tree Structure
Animashree Anandkumar, Kamalika Chaudhuri, Daniel Hsu +3
cs.LGstat.MLarXiv:1107.1283v22011
Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning
Zhilin Zhang, Bhaskar D. Rao
stat.MLcs.LGarXiv:1102.3949v22011
Robust PCA via Outlier Pursuit
Huan Xu, Constantine Caramanis, Sujay Sanghavi
cs.LGcs.ITstat.MLarXiv:1010.4237v22010
Robust Recovery of Subspace Structures by Low-Rank Representation
Guangcan Liu, Zhouchen Lin, Shuicheng Yan +3
cs.ITcs.CVcs.LGarXiv:1010.2955v62010
A survey of statistical network models
Anna Goldenberg, Alice X Zheng, Stephen E Fienberg +1
stat.MEcs.LGphysics.soc-pharXiv:0912.5410v12009
Graph Kernels
S. V. N. Vishwanathan, Karsten M. Borgwardt, Imre Risi Kondor +1
cs.LGarXiv:0807.0093v12008
R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization
Jingyi Zhang, Jiaxing Huang, Huanjin Yao +4
cs.AIcs.CLcs.CVarXiv:2503.12937v22025
Optimal CUR Matrix Decompositions
Christos Boutsidis, David P. Woodruff
cs.DScs.LGmath.NAarXiv:1405.7910v22014
ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
Amir Gholami, Kurt Keutzer, George Biros
cs.LGarXiv:1902.10298v32019
RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning
Zihan Wang, Kangrui Wang, Qineng Wang +15
cs.LGcs.AIcs.CLarXiv:2504.20073v22025
TradingAgents: Multi-Agents LLM Financial Trading Framework
Yijia Xiao, Edward Sun, Di Luo +1
q-fin.TRcs.AIcs.CEarXiv:2412.20138v72024
Deep learning-based synthetic-CT generation in radiotherapy and PET: a review
Maria Francesca Spadea, Matteo Maspero, Paolo Zaffino +1
physics.med-phcs.LGeess.IVarXiv:2102.02734v22021
MemoryWalker: Stop Training Agents on Contexts They Never Saw
Zinco J, Xunjie Zhu, Shen Huang +3
cs.LGcs.CLarXiv:2609.00865v12026

Machine Learning

Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments

Learning Combinatorial Optimization Algorithms over Graphs

Probabilistic Vehicle Trajectory Prediction over Occupancy Grid Map via Recurrent Neural Network

Deep Sets

Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing

Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning

An Introduction to Deep Learning for the Physical Layer

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Learning to Invert: Signal Recovery via Deep Convolutional Networks

Machine Learning of Linear Differential Equations using Gaussian Processes

Theory-guided Data Science: A New Paradigm for Scientific Discovery from Data

Understanding Deep Neural Networks with Rectified Linear Units

Product-based Neural Networks for User Response Prediction

A Survey of Multi-View Representation Learning

Deep Visual Foresight for Planning Robot Motion

Adversarial examples in the physical world

Context-Aware Proactive Content Caching with Service Differentiation in Wireless Networks

Going Deeper with Contextual CNN for Hyperspectral Image Classification

A survey of sparse representation: algorithms and applications

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Benefits of depth in neural networks

Variational Inference: A Review for Statisticians

The Power of Depth for Feedforward Neural Networks

The Limitations of Deep Learning in Adversarial Settings

The Extreme Value Machine

Optimizing Neural Networks with Kronecker-factored Approximate Curvature

LSTM: A Search Space Odyssey

Deep Learning and the Information Bottleneck Principle

Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval

Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Learning to Generate Chairs, Tables and Cars with Convolutional Networks

Neural Machine Translation by Jointly Learning to Align and Translate

Learning Deep Representation for Face Alignment with Auxiliary Attributes

Learning to Deblur

Combinatorial Multi-Armed Bandit and Its Extension to Probabilistically Triggered Arms

Auto-Encoding Variational Bayes

Dropout improves Recurrent Neural Networks for Handwriting Recognition

Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals

Stochastic blockmodel approximation of a graphon: Theory and consistent estimation

Deep Learning Through the Lens of Example Difficulty

Domain Generalization via Invariant Feature Representation

The Emerging Field of Signal Processing on Graphs: Extending High-Dimensional Data Analysis to Networks and Other Irregular Domains

Deep Learning for Detecting Robotic Grasps

Equivalence of distance-based and RKHS-based statistics in hypothesis testing

Sparse Distributed Learning Based on Diffusion Adaptation

Practical Bayesian Optimization of Machine Learning Algorithms

Diffusion Adaptation Strategies for Distributed Optimization and Learning over Networks

Spectral Methods for Learning Multivariate Latent Tree Structure

Sparse Signal Recovery with Temporally Correlated Source Vectors Using Sparse Bayesian Learning

Robust PCA via Outlier Pursuit

Robust Recovery of Subspace Structures by Low-Rank Representation

A survey of statistical network models

Graph Kernels

R1-VL: Learning to Reason with Multimodal Large Language Models via Step-wise Group Relative Policy Optimization

Optimal CUR Matrix Decompositions

ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs

RAGEN: Understanding Self-Evolution in LLM Agents via Multi-Turn Reinforcement Learning

TradingAgents: Multi-Agents LLM Financial Trading Framework

Deep learning-based synthetic-CT generation in radiotherapy and PET: a review

MemoryWalker: Stop Training Agents on Contexts They Never Saw